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Effects of Task Difficulty on Kinematics and Task Performance during Walking Workstation Use

Harry, John, R.1; Eggleston, Jeffrey, D.2; Dunnick, Dustin, D.2; Edwards, Hannah2; Dufek, Janet, S.2

Translational Journal of the American College of Sports Medicine: June 1, 2018 - Volume 3 - Issue 11 - p 74–84
doi: 10.1249/TJX.0000000000000062
Original Investigation
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SDC

Although walking workstations do not seem to compromise task performance despite altered gait kinematics, current evidence stems from evaluations of relatively simple tasks that do not reflect typical work duties.

Purpose This study aimed to examine the effects of simple cognitive (SC) and complex cognitive (CC) tasks on gait kinematics during walking workstation use in comparison to baseline walking.

Methods Three-dimensional kinematic data of the lower extremity and trunk were collected while walking during baseline, SC, and CC conditions, with each condition performed at a self-selected velocity. Kinematic data were time normalized to 100% of the gait cycle and divided into subphases for analysis. Differences in walking velocity (baseline vs SC/CC) and task performance (SC vs CC) were tested using paired-samples t-test (α = 0.05). Kinematic data were tested for differences between baseline and SC, baseline and CC, and SC and CC using a point-to-point model statistic analysis (α = 0.05) at the single-subject level.

Results Walking velocity was not different between baseline and SC/CC (1.10 ± 0.25 m·s−1, baseline; 1.11 ± 0.26 m·s−1, SC/CC; P = 0.409), nor was task performance time different between SC and CC (81.1 ± 25.6 s, SC; 87.6 ± 17.7 s, CC; P = 0.394). Similar percentages of differences were detected across participants during each gait subphase for all lower extremity joint angles during SC and CC when compared with baseline. A greater percentage of differences were observed in trunk angles during SC than during CC when compared with baseline.

Conclusions Results indicate that trunk kinematics are influenced by task difficulty during walking workstation use, although lower extremity kinematics are not affected regardless of task difficulty. Thus, walking workstations do not compromise task performance during work-related tasks and walking safety does not seem threatened by tasks of greater difficulty.

1Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, TX; and

2Department of Kinesiology and Nutrition Sciences, The University of Nevada, Las Vegas, Las Vegas, NV

Address for correspondence: John R. Harry, Ph.D., Department of Kinesiology and Sport Management, Texas Tech University, 3204 Main St., Lubbock, TX 79409-3011 (E-mail: john.harry@ttu.edu).

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INTRODUCTION

There has been a dramatic increase in the amount of time spent performing deskbound, work-related activities in the United States (1,2). Deskbound activities promote little to no physical activity due to the predominance of seated or reclined postures (1–3). This lack of physical activity has contributed to the continual increase in obesity rates (1), the risk for developing diabetes (4,5), cardiovascular disease, and increased mortality rates (5). Accordingly, there have been efforts to increase physical activity in individuals working in these environments by using activity-promoting workstations. In comparison to both sitting and standing, active workstations promote markedly increased energy expenditure (6–8) to counter the sedentary characteristics of the typical work environment. Such workstations include adjustable sit-to-stand workstations (9,10) and various styles of active workstations that incorporate a treadmill (11–14), elliptical (11), or cycle ergometer (6,11,15).

A number of studies suggest that active workstations have little to no effects on executive function (command and control of cognitive skills), such as Wisconsin Card Sorting and reading comprehension, or computer-based cognitive task performance, such as the Stroop and Flanker test (16–18). However, Straker et al. (6) documented a 6% decrease in typing speed while performing a 3-min copy typing test during walking workstation use, in addition to a 3% increase in typing error rate. A 14% decrease in mouse performance was also observed, with a 106% increase in error rate (6). Thus, whether or not active workstations lead to compromised task performance during work-related tasks remains unclear. This lack of clarity may be due to the fact that no study to date has evaluated working tasks that closely reflect the cognitive demands of typical work duties.

On the basis of the number of available studies, walking workstation use seems to be the most implemented active workstation variant (6–9,12–20). Because users are exposed to dual-task demands during walking workstation use, changes in walking mechanics as a result of workstation use should be quantified (12). Walking workstation use might coincide with an increased risk for acute gait-related adverse events due to dual-task demands of walking and completing more cognitively demanding tasks (12,21–24). For instance, in comparison to baseline conditions, walking velocity was decreased when participants were asked to concurrently spell words backward (21,25–27). Observed decreases in velocity may be due to altered gait mechanics (12,21,22,24). However, when users were asked to identify their preferred walking velocity before performing a secondary task, velocity was not shown to be different from baseline, although differences in walking mechanics were observed (12). The performance of various work tasks is typically not compromised during walking workstation use (14,16,17). Thus, walking mechanics, and not task performance (12), might be altered during walking workstation use. However, the performance of relatively simple tasks during walking workstation use, such as navigating a website (12) or the Wisconsin Card Sorting test (17), might not provide a sufficient cognitive challenge to inducing changes in both walking mechanics and task performance. We presume that more challenging work tasks would further alter gait kinematics in comparison to more simple cognitive (SC) tasks.

Previous research has shown that participants were generally able to adjust their gait patterns after approximately 30 s of exposure to a walking workstation (12). As described previously, the simplicity of the working tasks during completed during those tests was the driving force behind the short-term accommodation observed previously (12). If short-term accommodations are noted during simpler tasks but not during more complex tasks, it may be that performing complex tasks on a walking workstation would present a greater risk for experiencing a gait-related adverse event such as a trip, fall, or loss of balance during prolonged use. Therefore, the purpose of this study was to examine the effect of both SC and complex cognitive (CC) tasks on lower extremity gait kinematics during walking workstation use in comparison to baseline walking. The following hypotheses were tested: 1) task performance would be compromised during CC in comparison to SC; 2) altered gait kinematics would occur during both SC and CC in comparison to baseline, with gait kinematics being more greatly affected during CC; and 3) participants would demonstrate an acute kinematic accommodation during SC (12) but not CC due to the increased difficulty.

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METHODS

Study Participants

An a priori power analysis (G*Power v3.1, Dusseldorf, Germany) was performed to determine the necessary sample size for this study using the task completion time data between walking workstation use and standing presented by Dufek et al. (12). On the basis of a proposed effect size of 0.77, power (1 − β) of 0.80, and alpha (α) of 0.05, and a correlation between groups of 0.70, a total sample of 16 participants was determined necessary to ensure adequate statistical power. To ensure more than sufficient, 17 healthy adults (5 men and 12 women, 23.1 ± 2.7 yr, 1.7 ± 0.1 m, 71.9 ± 19.5 kg) were recruited to participate in this study. All were at least 18 yr of age and were able functionally ambulate on a treadmill without assistance for periods of 20 min. Before completing any laboratory activities, a description of the protocol was provided to the participants. Then, written informed consent was provided to the research team as approved by the institutional review board at the affiliated institution (protocol number: 978997-1) in accordance with the Declaration of Helsinki.

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Procedures

Participants were asked to visit the laboratory for a single experimental session. Demographic and anthropometric data (age, mass, height, sex) were measured and recorded. A demonstration of the protocol was then provided to the participants to ensure that they were aware of the proper use of the walking workstation and its functionality (i.e., adjusting desk height and walking velocity). The current study used a treadmill desk walking workstation, which was equipped with velocity and desk height controls (DZ9500; Signature Treadmill Desks, Fort Wayne, IN). A laptop computer (Hewlett-Packard, Palo Alto, CA) equipped with a wired keyboard and wireless mouse was positioned on the desk of the walking workstation. The laptop was connected to an external computer monitor to allow the participants to create a preferred working environment. Participants then completed a 5-min warm-up during which they were instructed to walk on the walking workstation at a self-selected pace. During the warm-up, participants were free to continue to adjust the desk height and computer monitor orientation to ensure that they were comfortable with the workstation setup. An exemplar video clip showing the use of the walking workstation is provided (see Video, Supplemental Digital Content 1, Demonstration of walking workstation use and functionality, http://links.lww.com/TJACSM/A21). After the warm-up, retroreflective markers were adhered to the following anatomical locations bilaterally: acromion process, iliac crest, anterior superior iliac spine, posterior superior iliac spine, medial and lateral aspects of the knee joint center, medial and lateral malleoli, and the base of the second metatarsal. Four-marker clusters were placed on the left and right thighs and legs, whereas three-marker clusters were placed on the heel counter of the shoes. Single markers were placed on the sternoclavicular notch and the sacrum. Three-dimensional kinematic data were obtained using a 10-camera motion capture system (200 Hz; Vicon Motion Systems, Ltd., Oxford, UK).

Participants completed a baseline walking condition during which they walked for a total of 5 min. During the first 4 min of the baseline condition, participants were free to adjust the treadmill velocity to identify their preferred pace. At the start of the fifth minute, 60 s of kinematic data was obtained to establish baseline gait characteristics. After the baseline condition, participants were provided a 1-min rest period before completing the SC and CC experimental conditions. The experimental conditions were presented to the participants in a counterbalanced order.

Both experimental conditions were performed during a single walking trial with 1-min of walking separating the conditions, during which data were not collected and the participants did not interact with the workstation. Specifically, participants walked for approximately 4 min to identify their preferred walking velocity in anticipation for the experimental conditions. At approximately 4 min and 30 s, the participants were instructed to make any final desk height and/or treadmill velocity adjustments. At the start of the fifth minute, no further adjustments were allowed and specific task instructions were delivered to the participants via e-mail. Once the participants verbally confirmed that they had received and opened the instructional e-mail, kinematic and condition-performance data collection began for the first experimental condition. Specific to SC, participants were instructed to visit the website of a local newspaper (The Las Vegas Sun; www.lasvegassun.com) by manually typing the full website address. Participants then navigated the website attempting to locate the “Politics” and “Entertainment” headline tabs. Once the specific headlines were located, participants copied and pasted the headlines in an e-mail reply to the original instructional e-mail. Specific to CC, the instructional e-mail included a type-written paragraph with instructions to copy and paste the paragraph into Microsoft Word (Microsoft Corporation, Redmond, WA) and revise the paragraph such that all sex-specific pronouns were switched (i.e., change “he” to “she” and vice versa). Once the document was determined by the participants to be completely revised, participants replied to the instructional e-mail message with the revised paragraph pasted into the body of the e-mail. For both experimental conditions, data collection was concluded when the participant verbally stated that they sent the response e-mail to the investigator. The preferred walking velocity and both SC/CC completion times were recorded for analysis. Extensive pilot testing was carried out to ensure that the SC and CC conditions were different with respect to difficulty/cognitive demand, particularly through the assessment of task completion time and subject difficulty scores.

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Data Processing

Raw data were exported to the Visual 3D Biomechanical Software Suite (C-Motion, Inc., Germantown, MD). A five-segment model was built from the raw marker trajectories to include the trunk, pelvis, thigh, leg, and foot segments. Only data from the right lower extremity were used for analysis. Raw marker trajectories were smoothed using a fourth-order low-pass Butterworth digital filter with a cut-off frequency of 6 Hz. From the smoothed marker trajectories, sagittal trunk angle was calculated as the angle of the thorax segment relative to the vertical axis of the laboratory. Sagittal plane hip, knee, and ankle joint angles were calculated as the angles of the thigh relative to the pelvis, the leg relative to the thigh, and the foot relative to the leg, respectively. All angles were expressed such that positive polarities represented a flexed/dorsiflexed position.

Data were then exported to Matlab (R2016b; The Mathworks Inc., Natick, MA). Kinematic data for each condition were reduced to strides using the treadmill velocity–based algorithm described by Zeni and colleagues (28). During the baseline condition, the average number of strides extracted across participants was 49.5 ± 11.1, ranging between 31 and 83 strides. The average number of strides extracted across participants during SC was 69.4 ± 26.0, ranging between 29 and 128 strides. For CC, the average number of strides evaluated across participants was 78.8 ± 26.7, ranging between 47 and 139 strides. Each stride was then normalized to 100% of the gait cycle (101 data points). Ensemble mean and SD time-histories were then computed across all strides per participant per condition for the trunk, hip, knee, and ankle angles. Ensemble mean and SD time-histories were also computed across the first, middle, and last blocks of 10 strides. The gait cycle was divided into subphases (29), representing the loading response (0–10%), mid stance (11%–30%), terminal stance (31%–50%), preswing (51%–60%), initial swing (61%–63%), mid swing (74%–87%), and terminal swing (88%–100%) phases of gait to examine if there was a specific subphase, or multiple subphases, of the gait cycle where more differences might occur between conditions.

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Statistical Analysis

Data are presented as mean ± 1 SD. Paired-samples t-tests (α = 0.05) were used to test for significant differences in both average walking velocities between baseline and SC/CC and the average task completion time between SC and CC across participants. To examine the acute effects of SC and CC on walking mechanics in comparison to baseline, a point-to-point analysis was performed at the single-subject level using the Model Statistic procedure (α = 0.05) (30). This procedure has been used frequently in recent years for gait analyses at the single-subject level (12,31–33); it can identify specific subphases of the gait cycle where anomalous lower extremity gait mechanics occur. Specifically, we compared each of the 101 data points of the mean ensemble curves between baseline and SC, between baseline and CC, and between SC and CC. The Model Statistic procedure is similar to a t-test, although it accounts for both the SD for each comparative mean and the number of strides used to calculate the critical difference for each individual on the basis of their own generated movement variability. Accordingly, there were 101 possible differences between the mean ensemble curves that were compared. The number of differences detected during each subphase was converted to a percentage of significant differences to describe the quantity of each subphase that was different between conditions. In addition, the average percentage of significant differences across the entire stride that was different between conditions was calculated across participants to provide the average kinematic response to the SC and CC conditions.

To determine whether participants exhibited short-term accommodations to the dual-task demands of the SC and/or CC conditions (12), we compared the first, middle, and last blocks of 10 strides within conditions for each kinematic parameter. Specifically, Model Statistic tests were performed at each of the 101 data points between the mean ensemble curves of the first 10 and middle 10 strides, the first 10 and last 10 strides, and the middle 10 and last 10 strides. Each of these comparisons was within conditions and between blocks of strides recorded during that condition. For one participant, the mean ensemble curve for the last block of strides during SC was calculated across only nine strides. This procedure was based on a recent comparison between ensemble curves for the first 10 and last 10 strides performed during walking workstation (12).

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RESULTS

Walking Velocity and Task Completion Time

No significant difference in walking velocity was detected between baseline and the experimental conditions (1.10 ± 0.25 m·s−1, baseline; 1.11 ± 0.26 m·s−1, SC/CC; P = 0.41). In addition, time to task completion showed no significant difference (P = 0.39) between the SC (81.1 ± 25.6 s) and CC (87.6 ± 17.7 s) conditions.

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Angular Position Comparisons to Baseline

Hip angular position differences between baseline and both SC and CC at the single-subject level are documented in Table 1. On average across participants during the loading response, mid stance, terminal stance, and preswing subphases of SC, 56.5% ± 39.9%, 69.2% ± 35.5%, 59.7% ± 32.6%, and 58.2% ± 35.4% of the subphases were different from baseline, respectively. During the initial swing, mid swing, and terminal swing subphases, 51.4% ± 34.1%, 69.5% ± 32.7%, and 54.6% ± 39.3% of the subphases were different from baseline, respectively. For CC, on average across participants during the loading response, mid stance, terminal stance, and preswing subphases, 53.3% ± 39.9%, 64.6% ± 36.3%, 51.6% ± 29.4%, and 60.8% ± 29.5% of the subphases were different from baseline, respectively. During the initial swing, mid swing, and terminal swing subphases, 59.0% ± 40.6%, 70.0% ± 33.6%, and 64.6% ± 32.7% of the subphases were different from baseline, respectively.

TABLE 1

TABLE 1

Knee angular position differences between baseline and both SC and CC at the single-subject level are documented in Table 2. For SC, on average across participants during the loading response, mid stance, terminal stance, and preswing subphases, 58.4% ± 36.6%, 50.0% ± 39.1%, 49.5% ± 36.4%, and 35.3% ± 35.1% of the subphases were different from baseline, respectively. During the initial swing, mid swing, and terminal swing subphases, 41.6% ± 36.5%, 48.0% ± 34.8%, and 53.7% ± 33.2% of the subphases were different from baseline, respectively. For CC, on average across participants during the loading response, mid stance, terminal stance, and preswing subphases, 57.3% ± 36.7%, 53.5% ± 39.1%, 42.7% ± 35.9%, and 39.8% ± 29.6% of the subphases were different from baseline, respectively. During the initial swing, mid swing, and terminal swing subphases, 42.8% ± 36.1%, 46.0% ± 33.9%, and 55.4% ± 29.9% of the subphases were different from baseline, respectively.

TABLE 2

TABLE 2

Ankle angular position differences between baseline and both SC and CC at the single-subject level are documented in Table 3. For SC, on average across participants during the loading response, mid stance, terminal stance, and preswing subphases, 48.0% ± 35.0%, 51.4% ± 39.5%, 50.3% ± 31.2%, and 54.3% ± 31.9% of the subphases were different from baseline, respectively. During the initial swing, mid swing, and terminal swing subphases, 54.9% ± 29.1%, 47.1% ± 32.5%, and 52.2% ± 33.7% of the subphases were different from baseline, respectively. For CC, on average across participants during the loading response, mid stance, terminal stance, and preswing subphases, 43.9% ± 37.1%, 48.9% ± 36.1%, 42.3% ± 29.6%, and 56.1% ± 28.7% of the subphases were different from baseline, respectively. During the initial swing, mid swing, and terminal swing subphases, 56.6% ± 30.8%, 45.4% ± 35.5%, and 61.8% ± 28.5% of the subphases were different from baseline, respectively.

TABLE 3

TABLE 3

Trunk angular position differences between baseline and both SC and CC at the single-subject level are documented in Table 4. For SC, on average across participants during the loading response, mid stance, terminal stance, and preswing subphases, 66.9% ± 34.9%, 70.6% ± 35.7%, 77.4% ± 36.1%, and 70.0% ± 36.4% of the subphases were different from baseline, respectively. During the initial swing, mid swing, and terminal swing subphases, 55.5% ± 33.0%, 51.7% ± 30.2%, and 60.1% ± 28.8% of the subphases were different from baseline, respectively. For CC, on average across participants during the loading response, mid stance, terminal stance, and preswing subphases, 51.4% ± 39.3%, 59.8% ± 39.4%, 32.5% ± 38.9%, and 59.4% ± 37.6% of the subphases were different from baseline, respectively. During the initial swing, mid swing, and terminal swing subphases, 47.2% ± 33.2%, 41.2% ± 33.5%, and 46.5% ± 30.5% of the subphases were different from baseline, respectively.

TABLE 4

TABLE 4

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Kinematic Accommodations to Workstation Use

The average percentages of significant differences across participants for the comparisons among the first, middle, and last 10 blocks of strides during SC are documented in Figure 1. No pattern of accommodation was observed at the hip, knee, ankle, or trunk angular positions. The average percentages of significant differences across participants for the comparisons among the first, middle, and last 10 blocks of strides during CC are documented in Figure 2. Similar to SC, no pattern of accommodation was observed at the hip, knee, ankle, or trunk angular positions during CC.

Figure 1

Figure 1

Figure 2

Figure 2

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DISCUSSION

The purpose of the current study was to examine the effects of SC and CC working tasks on task performance and lower extremity gait mechanics during walking workstation use when compared with baseline walking. This analysis revealed that neither walking velocity between baseline and SC/CC nor task completion time between SC and CC was different. However, gait mechanics were different among the three conditions. Specifically, the data showed similar percentages of significant differences in hip, knee, and ankle joint angular positions during the SC and CC conditions when compared with baseline and averaged across participants. The trunk, however, was characterized by a greater percentage of significant differences on average during the SC condition compared with the CC condition in comparison to baseline.

Despite our hypothesis that task performance would be more greatly compromised during CC in comparison to SC, performance does not seem to be more greatly affected by a greater level of task difficulty during walking workstation use (8,19). Because the tasks used in the current study were selected to more closely represent typically used simple and complex tasks performed in working settings, walking workstations likely have little to no effect on performance during typical computer interaction tasks. However, it is possible that the performance of fine motor dexterity tasks, such as precision computer mousing, is sacrificed during workstation use (6,14,20). Accordingly, individuals who routinely perform fine dexterity tasks should slowly integrate the walking workstation to avoid compromised work performance.

In contrast to our second hypothesis, the mean percentages of differences at the hip joint during all subphases of gait were similar during SC and CC. Although this might suggest that both conditions induce similar gait alterations across participants, it is important to consider the individual significant differences between conditions at the single-subject level. For instance, five participants displayed a greater percentage of differences across the gait cycle at the hip joint during CC compared with SC, six participants displayed a lesser percentage of differences during CC, and the remaining six participants displayed equal percentages of differences during SC and CC. A similar pattern was observed at each subphase of the gait cycle. When interpreting the percentages of differences on average at the knee joint and ankle joints, a similar spread of responses to the SC and CC conditions was observed, as there was less consistency within and among the joints. Thus, gait mechanics at the hip, knee, and ankle do not seem to be more greatly affected by the greater difficulty of the CC condition during any subphase of the gait cycle. Although these percentages of differences observed herein indicate that these participants used gait patterns that were significantly different from baseline for approximately half of each subphase at each lower extremity joint, the cognitive demand of the work seems to have little influence on lower extremity joint motion during walking workstation use.

In comparison to the percentages of differences observed at the hip, knee, and ankle joints, a greater percentage of significant differences in the angular position of the trunk were detected on average across participants for both SC and CC with respect to baseline. Similar to the lower extremity joints, distinct responses to SC and CC were exhibited by each participant. Interestingly, a greater percentage of differences were detected on average across participants during SC than during CC when compared with baseline. It may be that the increased cognitive demand of the CC condition required these participants to focus on maintaining sufficient lower extremity motion to mitigate increased risks for an adverse gait-related event (trip, fall, loss of balance, etc.), thereby explaining the similar percentages of differences between SC and CC when compared with baseline. Thus, the heightened focus on maintaining adequate lower extremity motion allowed the trunk move more freely throughout the gait cycle. As such, it may be that the participants perceived the SC task to be less challenging than CC (as would be expected), allowing them to be more focused on maintaining a stable upper extremity posture as they completed the SC task.

Contrary to our third hypothesis, the comparison among the first, middle, and last 10 strides during both experimental conditions did not reveal kinematic accommodations during either SC and CC on average across participants. In addition, the large SDs shown in Figures 1 and 2 indicate that none of the differences among blocks of strides for either condition were similar across participants. We conjecture that this result was due to the novelty of the walking workstation independent of the type of task performed or the difficulty of the tasks. As such, these participants were likely focused on controlling their body motions throughout the entire duration of the SC and CC conditions, prohibiting any kinematic accommodations from occurring. It may be that even longer duration tasks are necessary to observe notable and consistent kinematic accommodations during walking workstation use, as tasks that require approximately 81 and 87 s (SC and CC herein) or approximately 42 s (12) do not show explicit kinematic accommodations among participants using a walking workstation. Although our purpose was to require participants to perform more challenging tasks that more closely reflect true working tasks, it is possible that the SC and CC conditions were not challenging enough to elicit condition-dependent changes in performance and/or gait kinematics or kinematic accommodations. It should be noted that the percentages of differences compared with baseline and the lack of kinematic accommodations during either condition suggests that the risk for experiencing a gait-related trip, fall, or loss of balance exists and remains consistent during walking workstation use. However, none of the current participants showed gait patterns that could be characterized visually as “high-risk” gait patterns. In addition, these data indicate that any risk for such an event is not dependent on SC or CC conditions. It should be noted that the current participants could have had different levels of familiarity to the walking workstation. Such familiarity differences can be observed in Tables 1–4, particularly the relatively large amount of variability across participants (group SD) for the number of significant differences relative to the baseline walking condition. However, the within-subject (group analyses) and single-subject (individual participant) analyses used herein are not sensitive to the potential influence of familiarity differences among participants.

In summary, the current study revealed that simple and complex work tasks induce a similar number of differences in gait kinematics at the hip, knee, ankle, and trunk in comparison to baseline walking. In addition, increasing the difficulty of a work task does not seem to influence walking velocity or task performance. Accordingly, it is concluded that gait kinematics are altered during walking workstation use solely (in the context of the current study) due to the inclusion of a secondary work task. More challenging work tasks might require participants to focus more on their lower extremity movements than their trunk movements in comparison to less challenging work tasks. Despite varying levels of difficulty, kinematic accommodations do not seem to occur while completing simple and complex tasks during walking workstation use, at least when the tasks require less than 90 s to complete. When using a walking workstation, users might not need to be concerned with a loss of productivity. Finally, users might not need to be concerned with an increased risk for experiencing a trip, fall, or loss of balance when more challenging tasks are completed during workstation use.

This research was partially supported through a Doctoral Graduate Research Award from the Graduate College, University of Nevada, Las Vegas, Las Vegas, NV. The funder had no role in the study design, collection, analysis, or interpretation of the data, or in writing of the manuscript. The authors also express thanks to M. Lounsbery, Ph.D., for her initial guidance in development of this research concept.

The authors have no conflicts of interests to disclose. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

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REFERENCES

1. Church TS, Thomas DM, Tudor-Locke C, et al. Trends over 5 decades in U.S. occupation-related physical activity and their associations with obesity. PLoS One. 2011;6(5):e19657.
2. Parry S, Straker L. The contribution of office work to sedentary behaviour associated risk. BMC Public Health. 2013;13(1):296.
3. Barnes J, Behrens TK, Benden ME, et al. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab. 2012;37(3):540–2.
4. Hamilton MT, Hamilton DG, Zderic TW. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes. 2007;56(11):2655–67.
5. Wilmot E, Edwardson C, Achana F, et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia. 2012;55(11):2895–905.
6. Straker L, Levine J, Campbell A. The effects of walking and cycling computer workstations on keyboard and mouse performance. Hum Factors. 2009;51(6):831–44.
7. Levine JA, Miller JM. The energy expenditure of using a “walk-and-work” desk for office workers with obesity. Br J Sports Med. 2007;41(9):558–61.
8. Cox RH, Guth J, Siekemeyer L, Kellems B, Brehm SB, Ohlinger CM. Metabolic cost and speech quality while using an active workstation. J Phys Act Health. 2011;8(3):332–9.
9. Alkhajah TA, Reeves MM, Eakin EG, Winkler EA, Owen N, Healy GN. Sit-stand workstations: a pilot intervention to reduce office sitting time. Am J Prev Med. 2012;43(3):298.
10. Mansoubi M, Pearson N, Biddle SJ, Clemes SA. Using sit-to-stand workstations in offices: is there a compensation effect? Med Sci Sports Exerc. 2016;48(4):720–5.
11. Commissaris DA, Knemann R, Hiemstra-van Mastrigt S, et al. Effects of a standing and three dynamic workstations on computer task performance and cognitive function tests. Appl Ergon. 2014;45(6):1570–8.
12. Dufek JS, Harry JR, Soucy M, Guadagnoli M, Lounsbery M. Effects of active workstation use on walking mechanics and work efficiency. J Nov Physiother. 2016;6(2).
13. Funk RE, Taylor ML, Creekmur CC, Ohlinger CM, Cox RH, Berg WP. Effect of walking speed on typing performance using an active workstation. Percept Mot Skills. 2012;115(1):309–18.
14. Ohlinger CM, Horn TS, Berg WP, Cox RH. The effect of active workstation use on measures of cognition, attention, and motor skill. J Phys Act Health. 2011;8(1):119–25.
15. Parry S, Straker L, Gilson ND, Smith AJ. Participatory workplace interventions can reduce sedentary time for office workers—a randomised controlled trial. PLoS One. 2013;8(11):e78957.
16. Alderman BL, Olson RL, Mattina DM. Cognitive function during low-intensity walking: a test of the treadmill workstation. J Phys Act Health. 2014;11(4):752–8.
17. Ehmann P, Brush C, Olson R, Bhatt S, Banu A, Alderman B. Active workstations do not impair executive function in young and middle-age adults. Med Sci Sports Exerc. 2016;49(5):965.
18. Koepp GA, Manohar CU, McCrady-Spitzer SK, et al. Treadmill desks: a 1-year prospective trial. Obesity. 2013;21(4):705–11.
19. Torbeyns T, Bailey S, Bos I, Meeusen R. Active workstations to fight sedentary behaviour. Sports Med. 2014;44(9):1261–73.
20. John D, Bassett D, Thompson D, Fairbrother J, Baldwin D. Effect of using a treadmill workstation on performance of simulated office work tasks. J Phys Act Health. 2009;6(5):617–24.
21. Hollman JH, Kovash FM, Kubik JJ, Linbo RA. Age-related differences in spatiotemporal markers of gait stability during dual task walking. Gait Posture. 2007;26(1):113–9.
22. Grin-Lajoie M, Richards CL, McFadyen BJ. The negotiation of stationary and moving obstructions during walking: anticipatory locomotor adaptations and preservation of personal space. Motor Control. 2005;9(3):242–69.
23. Lindenberger U, Marsiske M, Baltes PB. Memorizing while walking: increase in dual-task costs from young adulthood to old age. Psychol Aging. 2000;15(3):417.
24. Schrodt LA, Mercer VS, Giuliani CA, Hartman M. Characteristics of stepping over an obstacle in community dwelling older adults under dual-task conditions. Gait Posture. 2004;19(3):279–87.
25. Faulkner KA. Multitasking: association between poorer performance and a history of recurrent falls. J Am Geriatr Soc. 2007;55(4):570–6.
26. Pashler H. Dual-task interference in simple tasks. Psychol Bull. 1994;116(2):220–44.
27. Yogev‐Seligmann G, Hausdorff JM, Giladi N. The role of executive function and attention in gait. Mov Disord. 2008;23(3):329–42.
28. Zeni JA, Richards JG, Higginson JS. Two simple methods for determining gait events during treadmill and overground walking using kinematic data. Gait Posture. 2008;27(4):710–4.
29. Gronley JK, Perry J. Gait analysis techniques rancho los amigos hospital gait laboratory. Phys Ther. 1984;64(12):1831.
30. Bates BT, James CR, Dufek JS. Single-subject analysis. Innov Anal Hum Move. 2004:3–28.
31. Eggleston JD, Harry JR, Hickman RA, Dufek JS. Analysis of gait symmetry during over-ground walking in children with autism spectrum disorder. Gait Posture. 2017;55:162–6.
32. Dufek JS, Eggleston JD, Harry JR, Hickman RA. A comparative evaluation of gait between children with autism and typically developing matched controls. Med Sci (Basel). 2017;5(1):1.
33. Bates BT, Dufek JS, James CR, Harry JR, Eggleston JD. The influence of experimental design on the detection of performance differences. Meas Phys Educ Exerc Sci. 2016;20(4):200–7.

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